Dependable Dempster-Shafer Inference Framework for Urban Air Quality Monitoring

Le, TH; Nguyen, HAD; Ha, QP; Ahmed, M; Barthelemy, X; Jiang, N; Duc, H; Azzi, M; Riley, M

Dependable Dempster-Shafer Inference Framework for Urban Air Quality Monitoring

Le, TH Nguyen, HAD Ha, QP Ahmed, M Barthelemy, X Jiang, N Duc, H Azzi, M Riley, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2025, PP, (99), pp. 1-1
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Le, TH
dc.contributor.author	Nguyen, HAD
dc.contributor.author	Ha, QP
dc.contributor.author	Ahmed, M
dc.contributor.author	Barthelemy, X
dc.contributor.author	Jiang, N
dc.contributor.author	Duc, H
dc.contributor.author	Azzi, M
dc.contributor.author	Riley, M
dc.date.accessioned	2025-07-15T22:06:29Z
dc.date.available	2025-07-15T22:06:29Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Sensors Journal, 2025, PP, (99), pp. 1-1
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/188416
dc.description.abstract	Observations of air pollution have become an increasing concern for authorities and citizens due to emissions from population growth and urbanization. In this regard, low-cost wireless sensor networks have emerged as a popular, cost-effective solution for monitoring and estimation of air pollutant levels in local areas. Due to environmental vulnerability, ensuring the required performance and reliability of these sensing devices remains an open problem. This paper presents the development of a dependable Dempster-Shafer inference (DDSI) framework based on evidence theory for colocated low-cost sensors for air quality monitoring. This approach facilitates fault detection and accounts for uncertainty to enhance the performance of the sensor network. A switching mechanism is employed in the inference layer to select the most reliable information based on the probability of the current operational status of sensors, thereby leveraging the resilience and data integrity of the monitoring system. The proposed method is comprehensively validated in both laboratory and real-world settings. The DDSI framework is then tested in monitoring humidity, temperature and particulate matters in the suburbs. The results are benchmarked with data collected from state-run air quality monitoring stations. Statistical analysis is conducted to show the accuracy of the proposed framework and a high alignment with station observations, achieving an R<sup>2</sup> of up to 0.918 for meteorological parameters and 0.892 for fine particles (PM<inf>2.5</inf>).
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2025.3577122
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	40 Engineering
dc.title	Dependable Dempster-Shafer Inference Framework for Urban Air Quality Monitoring
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	in_progress	*
dc.date.updated	2025-07-15T22:06:24Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Observations of air pollution have become an increasing concern for authorities and citizens due to emissions from population growth and urbanization. In this regard, low-cost wireless sensor networks have emerged as a popular, cost-effective solution for monitoring and estimation of air pollutant levels in local areas. Due to environmental vulnerability, ensuring the required performance and reliability of these sensing devices remains an open problem. This paper presents the development of a dependable Dempster-Shafer inference (DDSI) framework based on evidence theory for colocated low-cost sensors for air quality monitoring. This approach facilitates fault detection and accounts for uncertainty to enhance the performance of the sensor network. A switching mechanism is employed in the inference layer to select the most reliable information based on the probability of the current operational status of sensors, thereby leveraging the resilience and data integrity of the monitoring system. The proposed method is comprehensively validated in both laboratory and real-world settings. The DDSI framework is then tested in monitoring humidity, temperature and particulate matters in the suburbs. The results are benchmarked with data collected from state-run air quality monitoring stations. Statistical analysis is conducted to show the accuracy of the proposed framework and a high alignment with station observations, achieving an R² of up to 0.918 for meteorological parameters and 0.892 for fine particles (PM2.5).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/188416